Search

Text input is a desired feature for AR glasses. While there already exist various input modalities (e.g., voice, mid-air gesture), the diverse demands required by different input scenarios can hardly be met by the small number of fixed input postures offered by existing solutions. In this paper, we present Handwriting Velcro, a novel text input solution for AR glasses based on flexible touch sensors. The distinct advantage of our system is that it can easily stick to different body parts, thus endowing AR glasses with posture-adaptive handwriting input. We explored the design space of on-body device positions and identified the best interaction positions for various user postures. To flatten users' learning curves, we adapt our device to the established writing habits of different users by training a 36-character (i.e., A-Z, 0-9) recognition neural network in a human-in-the-loop manner. Such a personalization attempt ultimately achieves a low error rate of 0.005 on average for users with different writing styles. Subjective feedback shows that our solution has a good performance in system practicability and social acceptance. Empirically, we conducted a heuristic study to explore and identify the best interaction Position-Posture Correlation. Experimental results show that our Handwriting Velcro excels similar work [6] and commercial product in both practicality (12.3 WPM) and user-friendliness in different contexts. © 2023 ACM.

The last few decades have witnessed an emerging trend of wearable soft sensors; however, there are important signal-processing challenges for soft sensors that still limit their practical deployment. They are error-prone when displaced, resulting in significant deviations from their ideal sensor output. In this work, we propose a novel prototype that integrates an elbow pad with a sparse network of soft sensors. Our prototype is fully bio-compatible, stretchable, and wearable. We develop a learning-based method to predict the elbow orientation angle and achieve an average tracking error of 9.82 degrees for single-user multi-motion experiments. With transfer learning, our method achieves the average tracking errors of 10.98 degrees and 11.81 degrees across different motion types and users, respectively. Our core contributions lie in a solution that realizes robust and stable human joint motion tracking across different device displacements. © 2023 ACM.